The growing prevalence of mobile smart devices and the rapid advancement of social networking applications result in the phenomenal growth of the data generated at the edge network. To draw useful information from such geographically distributed data, federated learning (FL) has emerged as a promising paradigm that allows participating users to collaboratively learn a shared model, while keeping all the private training data on their edge devices. In particular, all the participants are allowed to run stochastic gradient descent (SGD) locally and send the intermediate gradients to the server periodically for global synchronization. The recent advances in mobile edge computing further facilitate the implementation of FL in mobile networks, since modern smart mobile devices are now armed with high-performance central processing units (CPUs) and graphics processing units (GPUs) to handle intensive computations of intelligent applications. With the technical advantages and implemental feasibilities, FL has seen recent successes in several applications, including next word prediction in Google’s Gboard [1]}, vocal classifier for “Hey Siri” [2]}, mobile augmented reality [3]}, etc.
